Combined-order hidden Markov models for reverberation-robust speech recognition

Conference contribution
(Conference Contribution)


Publication Details

Author(s): Maas R, Kotha SR, Sehr A, Kellermann W
Publication year: 2012
Pages range: 167-171
ISBN: 978-1-4673-1878-5
Language: English


Abstract


In this contribution, the concept of combined-order hidden Markov models (CO-HMMs) is introduced by joining the first-order Markov and the second-order conditional independence assumption. The proposed approach is motivated and evaluated in the context of reverberation-robust automatic speech recognition. Two predecessor-dependent output probability density functions per hidden Markov model (HMM) state are employed in order to explicitly cope with the high inter-frame correlation in presence of reverberation. At the same time, the state duration modeling related to the first-order Markov assumption is addressed by a recently published training procedure based on hard alignment having the significant advantage that any conventional HMM can be efficiently updated to a CO-HMM. The experimental results show a reduction in average entropy as well as in word error rate in reverberant environments compared to conventional HMMs. © 2012 IEEE.


FAU Authors / FAU Editors

Kellermann, Walter Prof. Dr.-Ing.
Professur für Nachrichtentechnik
Kotha, Sujan Reddy
Lehrstuhl für Multimediakommunikation und Signalverarbeitung
Maas, Roland
Lehrstuhl für Multimediakommunikation und Signalverarbeitung
Sehr, Armin Dr.-Ing.
Professur für Nachrichtentechnik


How to cite

APA:
Maas, R., Kotha, S.R., Sehr, A., & Kellermann, W. (2012). Combined-order hidden Markov models for reverberation-robust speech recognition. (pp. 167-171). Baiona, ES.

MLA:
Maas, Roland, et al. "Combined-order hidden Markov models for reverberation-robust speech recognition." Proceedings of the International Workshop on Cognitive Information Processing (CIP), Baiona 2012. 167-171.

BibTeX: 

Last updated on 2018-10-12 at 20:50